Enterprise-wide AI Risk Management (EW-AiRM)

PRAISE FOR ENTERPRISE-WIDE AI RISK MANAGEMENT “As the chair of the original House of Lords Select Committee on Artificial Intelligence, I have long argued for good AI governance—and EW-AiRM™ delivers exactly that. Professor Markus Krebsz has built a rigorous, practitioner-ready architecture that speaks the language risk professionals actually use, anchored in UNESCO’s ethical framework and the international regulatory instruments he helped design. For any board, CRO or compliance officer serious about closing the gap between the appearance of AI governance and its reality, this is the book they need.” —LORD CLEMENT-JONES CBE, House of Lords spokesperson for Science, Innovation and Technology; Chair, House of Lords Select Committee on Artificial Intelligence (2017–2018); author of Living with the Algorithm “As enterprises delegate ever more consequential decisions to AI, the discipline of governing those systems must keep pace with their capability. Markus Krebsz brings the practical scaffolding to help boards and risk functions. This is a rigorous, usable guide to closing the gap between principle and practice, and it earns its place on every risk leader’s desk.” —COLIN PAYNE, Head of Innovation, Financial Conduct Authority; Chairman, Global Financial Innovation Network “Markus Krebsz combines deep risk expertise and scholarly rigor to deliver a sophisticated, enterprise-wide framework for managing AI-risk-related challenges. By augmenting established ERM principles with a multi-layered approach and ethical filter, EW-AiRM™ transforms AI risk management into practical, integrated solutions. This is essential reading for risk professionals, executives, board members, and regulators.” —DR BOB MARK, Managing Partner, Black Diamond Risk; co-author of The Essentials of Risk Management “As AI becomes embedded in organizational decision-making, governance needs to become more practical, measurable, and accountable. EW-AiRM™ provides a useful framework for that transition, connecting enterprise risk management with AI risk identification and mitigation in a format designed for practitioners.” —PETER SLATTERY, PhD, Research Scientist, MIT AI Risk Initiative, MIT FutureTech “EW-AiRM™ offers senior risk leaders a practical, enterprise-wide approach to governing AI, focused on explainability, lifecycle monitoring, human oversight and operational resilience. Among its clearest contributions is treating agentic and multi-agent systems as ongoing control problems rather than one-off implementations, making it immediately actionable for boards, CROs and risk teams.” —TANVEER BHATTI, Former Managing Director and Global Head of Model Risk and Valuation Control, Citi; former Head of Group Model & AI Risk Management, Revolut “Good AI governance keeps a human at the centre, by design, not as an afterthought. Krebsz builds that conviction into structure, placing human oversight at the heart of every AI decision rather than bolting ethics on at the end. EW-AiRM™ is rigorous, board-ready, and refreshingly honest about what governance theatre costs us.” —DR MARTHA BOECKENFELD, Board Director, Insurance and Asset Management A structured governance framework for managing AI risk across your entire organization Enterprise-Wide AI Risk Management (EW-AiRMTM): A Practical Framework for Managing AI Risks in any Organisation, by Prof. Markus Krebsz, introduces a multi-layered governance framework forged from five years of United Nations participatory AI policy development and over thirty years of risk management experience. EW-AiRMTM augments existing ERM frameworks like ISO 31000, COSO, and the UK Orange Book with AI-specific governance capabilities. The book presents a risk taxonomy drawn from more than 1,700 categorised AI risks across seven evidence-based domains and 24 sub-domains, alongside over 800 risk mitigation controls organised into four actionable quadrants. Six foundational pillars establish governance prerequisites before AI deployment, while the HAiPECR ethical filter keeps human accountability central to every governance decision. Readers will also find: A resilience framework designed to prepare organisations for AI Black Swan events that cannot be fully anticipated in advance Practical guidance on extending existing enterprise risk management structures rather than replacing them with entirely new governance architectures A risk taxonomy spanning seven domains and 24 sub-domains built from over 1,700 categorised AI risk scenarios The HAiPECR ethical framework, hosted on the OECD.AI tool website, embedding human accountability into every governance decision Written for Chief Risk Officers, board members, compliance leaders, data scientists, and senior executives making AI deployment decisions, this book delivers a field-tested governance framework that addresses the AI-specific risks existing enterprise risk management structures were never designed to handle.